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Support vector machines for antenna array processing and electromagnetics

Support vector machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks. These techniques have been proved to have superior performances in a large variety of real world applications due to their generalization abilities and robustne...

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Bibliographic Details
Main Author: Martínez-Ramón, Manel, 1968-
Other Authors: Christodoulou, Christos G.
Format: Electronic
Language:English
Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2006.
Edition:1st ed.
Series:Synthesis lectures on computational electromagnetics (Online), #5.
Subjects:
Antenna arrays.
Electromagnetism.
Machine learning.
Multivariate analysis.
Signal processing > Statistical methods.
Online Access:Abstract with links to full text
Table of Contents:
  • 1. Introduction
  • 1.1. Motivation of this book
  • 1.2. Learning machines and generalization
  • 1.3. Organization of this book
  • 2. Linear support vector machines
  • 2.1. An intuitive explanation of the support vector classifier
  • 2.2. An intuitive explanation of the support vector regressor
  • 3. Nonlinear support vector machines
  • 3.1. The Kernel trick
  • 3.2. Construction of a nonlinear SVC
  • 3.3. Construction of a nonlinear SVR
  • 4. Advanced topics
  • 4.1. Support vector machines in the complex plane
  • 4.2. Linear support vector ARx
  • 4.3. Robust cost function of support vector regressors
  • 4.4. Parameter selection
  • 5. Support vector machines for beamforming
  • 5.1. Problem statement
  • 5.2. Linear SVM beamformer with temporal reference
  • 5.3. Linear SVM beamformer with spatial reference
  • 5.4. Nonlinear parameter estimation of linear beamformers
  • 5.5. Nonlinear SVM beamformer with temporal reference
  • 5.6. Nonlinear SVM beamformer with spatial reference
  • 6. Determination of angle of arrival
  • 6.1. Linear SVM AOA estimator using regression
  • 6.2. Nonlinear AOA estimators
  • 6.3. Nonlinear SVM estimator using multiclass classification
  • 7. Other applications in electromagnetics
  • 7.1. Buried object detection
  • 7.2. Sidelobe control
  • 7.3. Intelligent alignment of waveguide filters.

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